Academic Journal
Προβολή σε EDS

Bilevel Optimization and Heuristic Algorithms for Integrating Latent Demand into the Design of Large-Scale Transit Systems.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Bilevel Optimization and Heuristic Algorithms for Integrating Latent Demand into the Design of Large-Scale Transit Systems.
Συγγραφείς: Guan, Hongzhao1 (AUTHOR) hguan7@gatech.edu, Basciftci, Beste2 (AUTHOR) beste-basciftci@uiowa.edu, Van Hentenryck, Pascal1 (AUTHOR) pascal.vanhentenryck@isye.gatech.edu
Πηγή: Transportation Science (INFORMS). May/Jun2026, Vol. 60 Issue 3, p445-465. 21p.
Θεματικοί όροι: *Supply & demand, *Public transit, Bilevel programming, Heuristic algorithms
Περίληψη: Capturing latent demand has a pivotal role in designing transit services, as omitting these riders can lead to poor quality of service and/or additional costs. This paper explores this topic in the design of transit networks by considering the perspectives of both the transit agencies and riders. The paper presents a generic bilevel optimization model—namely, the Transit Networks Design with Adoptions (TN-DA)—that considers the network design decisions in the leader problem and routing of the riders in the follower problem under the given network design, while allowing a black-box choice function for representing the adoption behavior of latent demand. The paper then identifies structural properties of the optimal solution of the TN-DA problem, which are desirable for transit agencies for capturing adoption behavior of the riders. The paper further provides guideline metrics for the transit agencies based on these desired adoption properties. Because of the computational complexity of this bilevel problem, the paper proposes five efficient heuristic algorithms to solve large-scale instances, which leverage an iterative procedure by solving a simpler version of the TN-DA problem and integrating the evaluation of rider choices. These algorithms either satisfy the desired properties of the optimal solution or provide fast approximations. The paper presents extensive large-scale case studies on two different transit systems by utilizing real data sets: (i) On-demand Multimodal Transit Systems (ODMTS) and (ii) Scooters-Connected Transit Systems (SCTS). The results demonstrate that under time limits, the heuristic algorithms can find high-quality solutions satisfying key adoption properties of the optimal solutions much faster than the exact approaches over various large-scale instances of ODMTS and SCTS. Funding: Financial support from the National Science Foundation [Leap-HI Grant 1854684, NSF Grant 2112533, and Division of Civil, Mechanical and Manufacturing Innovation Grant 2434302] and the Department of Transportation [T-SCORE Grant 69A3552047141] is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2025.0194. [ABSTRACT FROM AUTHOR]
Copyright of Transportation Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Business Source Index
FullText Text:
  Availability: 0
Header DbId: bsx
DbLabel: Business Source Index
An: 193691045
RelevancyScore: 1492
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1491.61413574219
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Bilevel Optimization and Heuristic Algorithms for Integrating Latent Demand into the Design of Large-Scale Transit Systems.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Guan%2C+Hongzhao%22">Guan, Hongzhao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hguan7@gatech.edu</i><br /><searchLink fieldCode="AR" term="%22Basciftci%2C+Beste%22">Basciftci, Beste</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> beste-basciftci@uiowa.edu</i><br /><searchLink fieldCode="AR" term="%22Van+Hentenryck%2C+Pascal%22">Van Hentenryck, Pascal</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> pascal.vanhentenryck@isye.gatech.edu</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Transportation+Science+%28INFORMS%29%22">Transportation Science (INFORMS)</searchLink>. May/Jun2026, Vol. 60 Issue 3, p445-465. 21p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Supply+%26+demand%22">Supply & demand</searchLink><br />*<searchLink fieldCode="DE" term="%22Public+transit%22">Public transit</searchLink><br /><searchLink fieldCode="DE" term="%22Bilevel+programming%22">Bilevel programming</searchLink><br /><searchLink fieldCode="DE" term="%22Heuristic+algorithms%22">Heuristic algorithms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Capturing latent demand has a pivotal role in designing transit services, as omitting these riders can lead to poor quality of service and/or additional costs. This paper explores this topic in the design of transit networks by considering the perspectives of both the transit agencies and riders. The paper presents a generic bilevel optimization model—namely, the Transit Networks Design with Adoptions (TN-DA)—that considers the network design decisions in the leader problem and routing of the riders in the follower problem under the given network design, while allowing a black-box choice function for representing the adoption behavior of latent demand. The paper then identifies structural properties of the optimal solution of the TN-DA problem, which are desirable for transit agencies for capturing adoption behavior of the riders. The paper further provides guideline metrics for the transit agencies based on these desired adoption properties. Because of the computational complexity of this bilevel problem, the paper proposes five efficient heuristic algorithms to solve large-scale instances, which leverage an iterative procedure by solving a simpler version of the TN-DA problem and integrating the evaluation of rider choices. These algorithms either satisfy the desired properties of the optimal solution or provide fast approximations. The paper presents extensive large-scale case studies on two different transit systems by utilizing real data sets: (i) On-demand Multimodal Transit Systems (ODMTS) and (ii) Scooters-Connected Transit Systems (SCTS). The results demonstrate that under time limits, the heuristic algorithms can find high-quality solutions satisfying key adoption properties of the optimal solutions much faster than the exact approaches over various large-scale instances of ODMTS and SCTS. Funding: Financial support from the National Science Foundation [Leap-HI Grant 1854684, NSF Grant 2112533, and Division of Civil, Mechanical and Manufacturing Innovation Grant 2434302] and the Department of Transportation [T-SCORE Grant 69A3552047141] is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2025.0194. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Transportation Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=bsx&AN=193691045
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1287/trsc.2025.0194
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 21
        StartPage: 445
    Subjects:
      – SubjectFull: Supply & demand
        Type: general
      – SubjectFull: Public transit
        Type: general
      – SubjectFull: Bilevel programming
        Type: general
      – SubjectFull: Heuristic algorithms
        Type: general
    Titles:
      – TitleFull: Bilevel Optimization and Heuristic Algorithms for Integrating Latent Demand into the Design of Large-Scale Transit Systems.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Guan, Hongzhao
      – PersonEntity:
          Name:
            NameFull: Basciftci, Beste
      – PersonEntity:
          Name:
            NameFull: Van Hentenryck, Pascal
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 05
              Text: May/Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 00411655
          Numbering:
            – Type: volume
              Value: 60
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: Transportation Science (INFORMS)
              Type: main
ResultId 1